Data processing and data movement in cloud computing environment

ABSTRACT

A plurality of data sets to be moved from a source site to a target site in a cloud computing platform is received at a plurality of a containerized data ingest components located at the source site. The received plurality of data sets are provided from the plurality of data ingest components to a staging cluster comprising a plurality of containerized broker components located at the source site, wherein the plurality of containerized broker components queue the plurality of data sets. The queued plurality of data sets are provided from the plurality of containerized broker components to a processing cluster comprising a plurality of containerized data processing components, wherein the plurality of containerized data processing components process the plurality of data sets. The plurality of data sets is transmitted from the plurality of containerized data processing components to the target site.

FIELD

The field relates generally to data processing and data movement, andmore particularly to data processing and data movement in cloudcomputing environments.

BACKGROUND

Computing environments, such as data centers, frequently employ cloudcomputing platforms, where “cloud” refers to a collective computinginfrastructure that implements a cloud computing paradigm. For example,as per the National Institute of Standards and Technology, cloudcomputing is a model for enabling ubiquitous, convenient, on-demandnetwork access to a shared pool of configurable computing resources(e.g., networks, servers, storage, applications, and services) that canbe rapidly provisioned and released with minimal management effort orservice provider interaction. Cloud-based data centers are deployed andmanaged by cloud service providers, who provide a computing environmentfor customers (tenants) to run their application programs (e.g. businessapplications or otherwise). Such cloud computing platforms may beimplemented at least in part utilizing one or more virtual computeelements such as one or more virtual machines (VMs) or one or morecontainers. By way of example, one commonly used type of container is aDocker container.

In such a cloud computing platform, data may typically have to be movedacross one or more networks. Reasons for such data movement include, butare not limited to, data migration into or out of the cloud environment,cross-site data protection, or re-scheduling of workflow instances.

Enterprises (e.g., companies, institutions, etc.) typically have theirown “on-premises” computing platforms (as compared with an“off-premises” computing platform such as the above-described cloudcomputing platform or data center). Within the on-premises context,various data moving technologies have been developed and employed. Thesetraditional enterprise-level data moving techniques are designed to betightly coupled, efficient, and have rich features such as, e.g., datacompression and data deduplication. Such enterprise-level techniquestend to have sufficient recovery time objective (RTO) and recovery pointobjective (RPO) metrics. However, enterprise-level data movingtechniques may not always be adequate outside the on-premises context.

SUMMARY

Embodiments of the invention provide techniques for improved dataprocessing and data movement in cloud computing environments.

For example, in one embodiment, a method for moving data from a sourcesite to a target site in a cloud computing platform comprises thefollowing steps. A plurality of data sets to be moved from the sourcesite to the target site is received at a plurality of containerized dataingest components located at the source site. The received plurality ofdata sets are provided from the plurality of data ingest components to astaging cluster comprising a plurality of containerized brokercomponents located at the source site, wherein the plurality ofcontainerized broker components queue the plurality of data sets. Thequeued plurality of data sets are provided from the plurality ofcontainerized broker components to a processing cluster comprising aplurality of containerized data processing components, wherein theplurality of containerized data processing components process theplurality of data sets. The plurality of data sets is transmitted fromthe plurality of containerized data processing components to the targetsite. For each data ingest component of the plurality of data ingestcomponents, a respective pipeline is formed through the staging clusterand the processing cluster, and the staging cluster and the processingcluster are scalable to add or remove a pipeline depending on the numberof data ingest components providing data sets thereto. The source siteand the target site are implemented via one or more processing devicesoperatively coupled via a communication network.

Advantageously, illustrative techniques provide data processing and datamovement for a cloud computing platform which account for the dynamicprovisioning, scaling, and high availability features of a cloudcomputing platform.

These and other features and advantages of the invention will becomemore readily apparent from the accompanying drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates typical data movement via array-based replicator,splitter or gateway functionalities.

FIG. 2 illustrates a data processing and data movement architecture,according to an embodiment of the invention.

FIG. 3 illustrates an extensible plugin agent component at a datasource, according to an embodiment of the invention.

FIG. 4 illustrates a scalable data staging methodology, according to anembodiment of the invention.

FIG. 5 illustrates a data staging broker component, according to anembodiment of the invention.

FIG. 6 illustrates a scalable processing cluster, according to anembodiment of the invention.

FIG. 7 illustrates a processing task as a directed acyclic graph,according to an embodiment of the invention.

FIG. 8 illustrates a phase confirmation methodology, according to anembodiment of the invention.

FIG. 9 illustrates a processing platform used to implement a dataprocessing and data movement architecture, according to an embodiment ofthe invention.

DETAILED DESCRIPTION

Illustrative embodiments may be described herein with reference toexemplary cloud infrastructure, data repositories, data centers, dataprocessing systems, computing systems, data storage systems andassociated servers, computers, storage units and devices and otherprocessing devices. It is to be appreciated, however, that embodimentsof the invention are not restricted to use with the particularillustrative system and device configurations shown. Moreover, thephrases “cloud environment,” “cloud computing platform,” “cloudinfrastructure,” “data repository,” “data center,” “data processingsystem,” “computing system,” “data storage system,” “data lake,” and thelike as used herein are intended to be broadly construed, so as toencompass, for example, private and/or public cloud computing or storagesystems, as well as other types of systems comprising distributedvirtual infrastructure. However, a given embodiment may more generallycomprise any arrangement of one or more processing devices.

As mentioned above, traditional enterprise-level data movingtechnologies include efficient designs and rich features such ascompression, deduplication, etc., while maintaining sufficient RTO/RPOmetrics. Examples of such enterprise-level technologies include, but arenot limited to, built-in replication capabilities of certain storagearrays such as VNX® replicator from EMC Corporation (Hopkinton, Mass.),data splitter capabilities such as RecoverPoint® from EMC Corporation(Hopkinton, Mass.), or gateway capabilities such as Export from AmazonWeb Service™ (AWS).

However, most of these technologies are designed in a tightly-coupledmanner which significantly limits their scalability. Thus, in acomputing environment with many computing nodes, while some of theseenterprise-level data moving technologies may have virtual machine (VM)versions that enable improved deployment, such a deployment does notchange the nature of their otherwise monolithic architecture.Accordingly, it is realized herein that these cumbersome, monolithicenterprise-level technologies do not fit well in a dynamic provisioning,scalable, and highly available cloud environment.

FIG. 1 illustrates typical data movement via array-based replicator,splitter, and gateway functionalities. As mentioned above, the datamovement approaches illustrated in FIG. 1 are traditional approachesused at the enterprise level. As generally shown in computingenvironment 100, application programs (applications or apps) execute ata source site 110, as well as at a target site 120. The two sites arecoupled by a communication network 130. Assume data from the source site110 has to be replicated at the target site 120. Thus, data must bemoved across communication network 130. Replication, splitter, andgateway functionalities can be implemented at each site via VMs and/orphysical devices (physical box). It is to be understood that one datasite may or may not implement all three traditional data movementapproaches, but they are shown together in FIG. 1 simply forconvenience.

In general, data replication functionality, such as provided by VNX®replicator, typically includes asynchronous file system levelreplication technology that provides enterprises with the ability tohandle disastrous events by transferring file system responsibilities(e.g., from source site 110) to a disaster recovery site (e.g., totarget site 120). A data mover interconnect is a communication channelused to transfer data between the source and target sites. VNX®replicator works by sending periodic updates from one or more data movercomponents of the source file system (source site 110) to one or moredata mover components of the target file system (target site 120).

Data splitter functionality, such as RecoverPoint®, provides synchronousand asynchronous replication over Internet Protocol (IP) or FibreChannel networks enabling creation of point-in-time, Fibre Channel/iSCSILUN copies at local or remote sites using one or more storage systems.The RecoverPoint® splitter function is used to “split” applicationwrites and send a copy of the write to a RecoverPoint® Appliance (RPA).The splitter carries out this activity efficiently, with minimalperceivable impact on host performance, since all CPU-intensiveprocessing necessary for replication is performed by the RPA. Thus, acopy of an application write at the source site 110 is sent to thetarget site 120 over the communication network 130.

Gateway functionality, such as AWS® Export, provides a gateway servicethat accelerates transferring large amounts of data using physicalstorage appliances and utilizes customer-provided portable devices totransfer smaller data sets. For example, large and/or small transfersmay be made from the source site 110 to the target site 120 over thecommunication network 130 using AWS® Export.

These traditional enterprise-level approaches of array-basedreplication, application write splitting, or gateway transfers havelimited scalability (e.g., usually one or two nodes), and are adequatefor many traditional applications (since instance number is limited) orfor dedicated/over-provisioned computing environments. However, a cloudcomputing platform typically deals with massive amounts of applications,data sets and cost models in a highly scalable environment, which issignificantly different than the traditional enterprise-levelenvironment.

One of the key factors that limits scalability with such traditionalenterprise-level approaches is data processing tasks, such as dataingesting, deduplication, compression, encryption, indexing, etc., whichcannot be easily partitioned and scheduled at multiple nodes. Instead,in such traditional approaches, software modules are tightly coupledwithin a single node with a heavy tuning effort involved in multi-core,inter-processes communication, etc.

Infrastructure resources and most applications/services in a cloudenvironment are dynamically provisioned and scheduled, typically inseconds rather than hours or days. However, many traditionalenterprise-level data moving technologies are specially optimized forcustomized hardware, and cannot be easily decoupled to run invirtualized or even a containerized environment for rapid provisioningand minimized overhead. Additionally, in a cloud environment unlike atraditional enterprise-level environment, management and orchestration(M&O) is abstracted and integrated into a data center or cloud levelcontrol plane such as OpenStack, Mesos, etc.

Furthermore, many of the data processing tasks that need to be performedwhere important data is manipulated and/or moved across nodes/sites(e.g., data ingestion, data transfer, deduplication, etc.) are statefulin nature, as opposed to stateless whereby a “failure-restart” model caneasily be applied. As is known, stateful means an application, task,and/or node keeps track of the state of interaction (usually by settingvalues in a storage field designated for that purpose), while statelessmeans there is no record of previous interactions. Thus, it is realizedthat consideration must be given to such stateful tasks which call forcareful handling in terms of fault tolerance and efficiency, e.g.,assuming a node fails, what is the impact to the overall data executionflow, and how does the system recover or rebuild in-progress tasks.

Many existing data moving technologies serve specific and limited datasources, e.g., array-based replication only works for the specific arrayproduct family, and other data moving technologies may only work forblock/VM-based systems but not for file-based systems. Most of theseexisting technologies do not support cloud-native applications executingin a container (i.e., containerized applications). Considering the factthat there are many different kinds of applications and data sources ina data center, such as file, block, VM, container, database, etc., it isdesired to have a consolidated solution that serves all of the datasources and moving requirements, thus reducing costs in terms ofconfiguration, management and skill set re-learning.

To overcome the above-mentioned and other drawbacks, illustrativeembodiments provide a micro-service oriented data processing and datamoving framework for “as-a-service” level functionality. “As-a-service”refers to the cloud computing paradigm whereby one or more productsand/or processes (i.e., services) are delivered over via the cloud(e.g., over the Internet) rather than provided locally or on-premises.“Micro-service” refers to a method of developing software applicationsas a suite of independently deployable, small, modular services in whicheach service runs a unique process and communicates through awell-defined, lightweight mechanism to serve a particular goal. Inillustrative embodiments, such a framework is implemented in a cloudenvironment, such as one that executes one or more container clusters,which include features such as scalability, fault-tolerance,extensibility and fast deployment/scheduling.

FIG. 2 illustrates a data processing and data movement architecture,according to an embodiment of the invention. As shown in illustrativecomputing environment 200, it is assumed that there is a source site 210coupled to a target (peer) site 220 via communication network 230. In anillustrative embodiment, each site can be a separate cloud (or separateset of clouds) in an overall cloud computing platform. Alternatively,the sites could be part of one cloud. Each site comprises a dataprocessing and data movement architecture that serves to overcome theabove-mentioned and other drawbacks associated with traditional datamoving technologies.

As shown at source site 210, one or more application instances executingon the site comprise one or more agent components (agents) 212. In thisexample, the agents 212 are associated with rewritable (RW) and/orread-only (RO) instances of the applications. That is, in illustrativeembodiments, an application can have multiple instances with more thanone active instance (e.g., App1 has three active-RW instances or nodes)or multiple instances with one active instance and one standby instance(e.g., App2 has two instances or nodes, one RW and one RO). Eachinstance may be running in a physical device, a VM or a Dockercontainer. Thus, FIG. 2 shows two typical cases: (1) a scale-out typeapplication (App1) such as Cassandra (Apache-based distributed databasemanagement system) where each instance is rewritable, so an agent 212 isconfigured at each instance; and (2) an active-standby mode application(App2) such as MySQL where the standby instance is a RO replica of theactive RW instance (maybe with a lag), and for such case, to reduceoverhead on the active node, the agent 212 is configured at thestandby-RO node. As mentioned, each application instance may beexecuting in its own dedicated container. In the case of the use ofDocker containers, this is referred to as being “dockerized,” but isalso more generally referred to as being “containerized.”

Further, as shown at source site 210, a scalable data moving service 214comprises a staging cluster 216 and a processing cluster 218. Each ofthe staging cluster 216 and processing cluster 218 isdockerized/containerized (e.g., one or more components therein areexecuting in one or more containers). The staging cluster 216 comprisesa set of broker components (brokers) respectively coupled to the agents212. In this illustrative embodiment, each agent 212 is coupled to itsown dedicated broker (e.g., broker1, broker 2, broker3, . . . , etc.).The processing cluster 218 comprises a set of index componentsrespectively coupled to the brokers, a set of reduce componentsrespectively coupled to the index components, and a set of securitycomponents respectively coupled to the reduce components. Thus, for eachagent 212, there is a processing pipeline that comprises a broker, anindex component, a reduce (data reduction) component, and a securitycomponent.

As shown, each component in the processing cluster 218 can be configuredto enable or disable a specific data moving task. For example, the datareduction component handles data deduplication and/or data compressionto reduce the traffic over the network 230 or the storage footprint onthe target site 220. The security component handles data encryptionbefore transferring the data to the (public) network 230. The indexcomponent provides an indexing function typically for document. Forexample, the index can provide high level statistics such as abstractionfrom file (to be moved) attributes, e.g., owner, modify time, datalength; or the index can be a detailed index about the document content.Document indexing is input/output (IO) intensive. However, now the indexcomponent is already loaded into memory and can be fully re-used forindex building.

As shown at target site 220, a similar scalable data moving service 222is provided but wherein the processing pipelines are reversed ascompared with data moving service 214 at the source site 210.

Further, as shown at target site 220, the scalable data moving service222 comprises a staging cluster 224 and a processing cluster 226. Eachof the staging cluster 224 and processing cluster 226 isdockerized/containerized. The staging cluster 224 comprises a set ofbroker components (brokers). The brokers are respectively coupled to aset of security components, which are respectively coupled to a set ofreduce (data reduction) components, which are respectively coupled to aset of index components of the processing cluster 226. The indexcomponents are respectively coupled to one or more agent components(agents) 228. As at the source site 210, the agents 228 of the targetsite 220 are associated with RW and/or RO instances (nodes) of one ormore applications (e.g., App1, App 2, etc.) executing in one or morecontainers. For example, at the target site 220, the applications arereassembled such as with multiple RO or RW instances. Thus agents 228are configured the same as at the source site 210 to receive theincoming traffic.

As also shown in computing environment 200, a management andorchestration (M&O) controller 240 provides various functionalities forthe source site 210, target site 220, and communication network 230.Examples of functionalities of controller 240 include, but are notlimited to, provisioning, discovery, configuration, policyimplementation, monitoring, and administrator or user interface (e.g.,dashboard function).

Accordingly, illustrative embodiments provide data processing and datamovement functionalities “as-a-service” through (micro-) services 214and 222. It is to be understood that while environment 200 shows onesource site 210 and one target site 220, there can be more source sites(data sources) and/or more target sites (peers) similarly configured inenvironment 200. The sites may be coupled using Transmission ControlProtocol/Internet Protocol (TCP/IP) across a local and/or a publicnetwork. In an illustrative embodiment, with separation of the controlplane and the data plane, a primary focus of the data processing anddata movement architecture in FIG. 2 is the data plane wherein ascalable, reliable and extensible data channel is provided between adata source (e.g., source site 210) and one or more peers (e.g., targetsite 220).

As will be explained in further detail below, the three main componentsof the data processing and data movement architecture comprise: theagent components (plus any plugins) that provide flexibility andextensible data ingest; the staging cluster for data aggregation andreliable (“true-of-fact”) functionality for subsequent processing; andthe processing cluster for continuous CPU/memory intensive datatransformations with high fault-tolerance.

Accordingly, as shown in FIG. 2, a general data flow through thearchitecture is as follows:

1) Data source (src): various sources types supported via plugins→dataingest agents (with replica affinity)→partition to stagingcluster→processed by scalable processing cluster (pipeline model), thengo to peer site across network; and

2) Peer site: similar flow but in reverse order: stagingcluster→scalable processing cluster→agents to application(s).

The concept of scalability here means that the staging cluster and theprocessing cluster are scalable to add or remove a pipeline (a paththrough the staging cluster and processing cluster) depending on thenumber of data ingest components providing data sets thereto.

In an illustrative embodiment, the operation is a data push mode, but inan alternative embodiment, specific components can be configured to pulldata. As mentioned above, in illustrative embodiments, main componentsincluding agents, brokers, tasks, etc., are running in containers.Advanced central management and orchestration are integrated with thearchitecture using various M&O tools (e.g., provided by M&O controller240). The architecture can be deployed in a cloud computing platformwith an Information Technology as-a-service (ITaaS) implementation orany software defined data center (SDDC).

FIG. 3 illustrates an extensible plugin agent component at a datasource, according to an embodiment of the invention. As shown inarchitecture 300, a set of one or more data sources 310 (e.g., includinga Docker container, a file from a file system (FS), a data block, adatabase (DB), a VM, etc.) are coupled to an agent container 320. Agentcontainer 320 is an example of agent 212 or 228 shown in FIG. 2. Agentcontainer 320 comprises a representational state transfer (REST)application programming interface (API) 321, an agent daemon 323, a datasource plugin layer 325, one or more worker threads 327, and a datapartition module 329.

More particularly, the agent container 320 operates closely with anapplication to ingest data (in the case of the agent being deployed at asource site) and apply data (in the case of the agent being deployed ata target/peer site). The agent container 320 executes within a containerand exposes a REST control API 321 and TCP/IP data ports. The extensibleplugin layer 325 is a software module that supports various types ofdata sources 310, such as, but not limited to, Docker, FS/file, block,DB, VM. The plugin layer 325 also supports various ingesting approachesvia, e.g., checkpoint (ckpt), or tar/DBdump, or DB streaming, etc., andsupports flexible control of data flow, e.g., frequency, ingest pace,partition parameters, etc. Thus, it is the various plugins function thatmanage data source operational details such as, but not limited to:application status check (e.g., running, inactive, paused, etc.); andconsistent point (e.g., freeze, ckpt, resume) for data-moving purposes:such as CRIU for Docker, DevMapper-snap, KVM-snap, or specified tarfiles such as PostgreSQL PITR (Point-In-Time-Recovery, viapd_dump/archive_command), or even streaming mode such as PostgreSQL LDStreaming.

One example of operating steps performed by the agent container 320 isas follows (the numbers 1-5 below correspond to the numbering of stepsin FIG. 3):

-   -   1) User submits, via REST API 321, a data moving task using        pre-defined format specifying:        -   Source type, e.g., docker, PostgreSQL, FS, block, etc.;        -   Source instance or unique identifier (ID) such as Docker ID,            file path, etc.;        -   Target information such as, e.g., IP:port;        -   Ingest approach, e.g., default via ckpt, or tar/dump, or            streaming, etc.; and        -   Relevant parameters/service level agreement (SLA), e.g.,            ingesting every five minutes, perform end-end data cyclic            redundancy check (CRC), encryption, auto-pace, etc.    -   2) Agent daemon 323 parses the task command and checks the        plugin layer 325 (according to the source type in the task        command). The plugin layer 325 checks the source instance        status, and whether or not there is checkpoint support. If the        checkpoint approach is specified, then the plugin layer 325        takes the ckpt, and returns ckpt to the agent daemon 323. For        example, checkpoint/restore in userspace (CRIU) would take a        ckpt for a running Docker instance, or PSQL pd_dump/archive to        get the binary log tar files.    -   3) Daemon 323 launches one or more worker threads 327 and        dispatches data reading tasks.    -   4) The one or more worker threads 327 read data segments in        parallel and perform CRC, and optionally calculate a digest for        deduplication.    -   5) Data segments are partitioned (data partition module 329) to        the staging cluster, e.g., via {jobID, instanceID, Offset|Data        Digest}.

The architecture advantageously provides affinity and flexiblescheduling. More particularly, the agent wrapped as a container (320)can be flexibly controlled and scheduled, such as to reduce impact on anormal RW. The agent container 320 can be run on an RO node such as aMySQL RO replica. Other applications such as Cassandra support multipleRW nodes. If so, the agent can run on each node. With deep integrationwith a centralized service registry, the architecture can set anaffinity policy to control the Docker-agent to handle cases such asapplication instance scale-out/in or online moving. As mentioned above,target (peer) agents are similar to source agents, but with reverseddata direction.

FIG. 4 illustrates a scalable data staging methodology, according to anembodiment of the invention. More particularly, architecture 400illustrates interaction between agent components 410-1 and 410-2 andbroker components 412-1, 412-2, and 412-3 (part of a staging clustersuch as 216 in FIG. 2).

More particularly, data is ingested by agents and pushed into ascalable, reliable data cluster for staging purposes before furthertransformation or manipulation (in the processing cluster). A stagingcluster consists of a numbers of broker nodes, each broker takes over apiece of data in segments (configurable per task, such as about 32KBytes˜1 MBytes). For partitioned data when pushing into the stagingcluster, a typical partition-key may be: (1) a coarse granularity suchas jobID; or (2) a fine-grain granularity; for example, at each datasegment level, two methods could be used: (i) segment offset in originalfile/block, etc., so data is evenly-balanced across brokers, and (ii)segment content indicated by SHA-1 or CityHash, etc. for a fingerprint(in such a case, similar data is stored together, thus facilitatingsubsequent data deduplication). In either case, the offset or hash areembedded as metadata. Each segment has its primary broker and optionallyone or more (e.g., one to two) replicas. Data is acknowledged when asegment is safely saved on the primary broker and all replicas if soconfigured.

At each broker, data can be stored in append-style files, and can onlybe removed (by background threads) when: (1) data has been processed bythe processing cluster, indicated by “commit offset;” and (2) thereexists no other reference on that segment such as due to deduplication.

Thus, as shown in FIG. 4, illustrative ingesting steps are shown (thenumbers 1-4 below correspond to the numbering of steps in FIG. 4):

1) Agent1 (410-1) partitions segments via content. For each givensegment, the agent then calculates and embeds its fingerprint, thenpushes both the data and the fingerprint into its primary broker (inthis case broker1 or 412-1).

2) Primary broker 412-1 saves data locally (usually in a system cacheand asynchronous flush), and forwards the data to a replica broker (inthis case broker2 or 412-2) if configured in parallel.

3) Similarly to first replica, the data is saved locally and forwardedto the next replica (in this case broker3 or 412-3) if so configured.

4) When all replicas are done, an acknowledgment (ACK) is sent to theagent 410-1.

As illustrated, the partition key can be configured as one of two types:“content” (actually a hash value of content, as shown in agent1 410-1)or “offset.” Agent2 410-2 here is the example of a partition by datablock “offset.” As shown, multiple agents or tasks can be runningconcurrently on the same broker (staging) cluster.

FIG. 5 illustrates a data staging broker component, according to anembodiment of the invention. As shown in architecture 500, a brokercomprises a deduplication module 502, a data queue 504, a local datacache 508, a remote replication module 510, an asynchronous (async)flush component 512; append-only files 514, and Docker volume or volumeplugin 516.

The staging cluster (multiple brokers) functionalities are shared formany applications/agents such as via TopicID (from unique taskID, forexample). Basically, the staging cluster is IO/disk and memoryintensive, since it is running as a Docker container, whereby diskstorage can use either a local Docker volume or shared storage viaDocker vol-plugin (516). Thus, data (514) is persistent and highlyreliable with one or more replicas.

Deduplication (502) is optional for two possible purposes: one is toreduce any duplicate data storage footprint in the staging cluster(thus, reducing cost) and enable a large efficient cache (508) forhigher performance; the other is to reduce data traffic over thecommunication network. The fingerprint can be calculated at the agentside during partition, thus duplication data is naturally ingested intothe same broker. Further, a detailed hash comparison is performed sinceone broker accommodates many hash spaces (hash space is much greaterthan the number of brokers in the staging cluster), and a comparison ofdata depending on segment size versus hash collision probability may beperformed. If data duplication is found, the broker increases itsreference and keeps one unique data copy.

To reduce the memory footprint, local cache (508), remote replication(510), or asynchronous (async) flush (512), all point to single memoryinstance (in data queue 504) with reference and each maintains theiroffset.

The purpose of the above example is to reduce the memory copy betweendifferent sub-components. Another example will be given to show thedifference. With incoming new data blocks, one can make a memory copyand send the copy to a remote replication module (510). Here the datacontent is maintained in a single queue (504). The remote replicationmodule (510) maintains its pointer or actually offset with respect toits replication progress. If a copy is really needed, it may executedirect memory access (DMA) between the network card and data queue (504)to the network card. Local cache (508) maintains some index (e.g., hash)that points to the data content in the queue (504). Async flush module(512) also maintains its flush progress (offset) rather than makinganother copy.

FIG. 6 illustrates a scalable processing cluster, according to anembodiment of the invention. As shown in architecture 600, a stagingcluster 610 is coupled to a processing cluster 620.

Staging cluster 610, as described above, acts as a reliable(“true-of-fact”) data source, and processing cluster 620 thenmanipulates or transforms those data sets using various operations suchas, but not limited to, compression, encryption, data transforming, dataindexing and/or data analytics. In an illustrative embodiment, thearchitecture adopts a streaming/processing model with apublish-subscribe model, i.e., agents publish data (or change) intostaging cluster (with unique topic), and the processing clustercontinuously loads data and processes data.

The architecture also employs an appending log style. The processingtasks illustrated in processing cluster 620 (e.g., compression,encryption, etc.) usually are CPU/memory intensive, though some of themmay generate or persist data. In general, we consider all the datatransforming operations as “logs,” none of them alone or combined canchange the “true-of-fact” nature of the data, i.e., a data set in thestaging cluster stands for “what happened” and is thus immutable. Thismodel greatly simplifies fault-tolerance and exception handling as willbe further explained below.

FIG. 7 illustrates a processing task as a directed acyclic graph (DAG),according to an embodiment of the invention. As shown in FIG. 7,upstream tasks 702 (e.g., data loading and CRC checking) and downstreamtasks 704 (e.g., compression and encryption) are organized in nodes asDAG 700. In illustrative embodiments, the DAG may take the form of theBig Data processing framework of Apache Spark or Apache Storm. In suchan Apache Storm-like framework, data is partitioned into pieces anddistributed to many nodes (actually many threads in containers). UnlikeApache Spark, there is no shuffle (usually for orderBy/GroupBy/ReduceBypurposes) needed in the Storm-like framework for such data movingscenarios. The last or leaf task (in this case, encryption) in thetopology of DAG 700 usually sends the data to the peer site over thecommunication network. Note that a task can be enabled or disabled per agiven configuration, thus there might be different DAG topologiesrunning in the processing cluster.

FIG. 8 illustrates a phase confirmation methodology, according to anembodiment of the invention. As shown in architecture 800, anacknowledge plus commit (“Ack+Commit”) two-phase confirmation mechanismis provided. FIG. 8 shows normal or successful processing steps. First,in step 1, there is a centralized task to track each retrieved sourcedata's processing status (from the staging cluster). Only the last taskin the DAG has the responsibility of sending an acknowledgement to thetracking task, and the source data is not marked as “completed” untilall derived data pieces complete processing and reach the last task.Then, the tracking task commits the status to the staging cluster, whichindicates the source data could be removed (by background thread if nodeduplication reference).

In short, “Ack” is for the completion of the processing cluster internalstatus (bind with specific DAG and logic). “Commit” maps to an externalsignal and indicates that the source data can be safely removed. Both“Ack” and “Commit” can be performed in a batched manner.

Fault-tolerance and idempotent: it is important to provide highreliability and fault-tolerance. As illustrative embodiments implement a“log-style” processing principle and pub-sub model, simplified andscaled fault-tolerance can be provided as a failure detection→redoparadigm.

Failure detection: given a time-out setting (e.g., 30 seconds), anyexception either task failure, node crash, network unavailable, etc., ismarked as a “failure” in the tracking task, and stops the processingtask at the second phase “commit.” Therefore, source data at the stagingcluster remains there, and nothing is lost. Note that a specific taskfailure would cause auto-restart of that thread by the agent daemon.

Redo: if enabled, the tracking task instructs and re-loads the data fromthe staging cluster and redoes the DAG until it reaches the leaf task.Importantly, in a data moving scenario, it follows an idempotentprinciple in that multiple redo operations do not impact the finalresult.

Thus, as shown in FIG. 8, illustrative ingesting steps are shown (thenumbers 1-5 below correspond to the numbering of steps in FIG. 8):

1) Data is loaded from the source site staging broker cluster 810 intothe processing cluster 820 (such loading is tracked by TaskID thenOffset, the offset denotes the data's current processing progress).

2) Execute the data deduplication, compression, and encryption perconfiguration.

3) The dataset (already processed) is about to transfer to the peersite's staging broker cluster 830.

4) Peer site staging broker cluster 830 safely stores the dataset (suchas with 3-replica), then sends Ack message to the data transmission (Tx)module (such as via TCP/Socket ACK). This is phase 1 commit.

5) Data Tx module further commits (phase 2 commit) current processingoffset to the source site staging broker cluster 810, which means thosepieces of the dataset can be safely removed or reclaimed by the sourcestaging brokers, since they have been already safely moved to peer. Ifsome error occurs before phase 2 commit, data content remains in thesource site staging broker cluster 810 and can then re-do the DAG.

It is to be appreciated that the target or peer site has similarcomponents, i.e., staging cluster, processing cluster, and agents, butdata flows in reverse order as compared with the source site. Thestaging cluster at the peer site acts as another reliability(“true-of-fact”) component, thus any exceptions during, e.g.,decompression would not commit, and tasks can “redo” from the closetstaging cluster (rather than retrieving data across the remote network).It is also to be appreciated that one site can play both the source roleand the target role with a single cluster that shares thefunctionalities.

Accordingly, illustrative embodiments as described herein provide manyadvantages. Better scalability is realized due to the following: (1) apub-sub processing model is introduced into the data moving/replicationscenarios, and major components in the framework are loosely-coupled,such that each component is able to provide service with independentscale-out capability; (2) data sets are partitioned (either offset orcontent) at the beginning, and thus loads are shared by many nodes; (3)separation of stateful tasks (data staging) versus stateless tasks(in-memory processing); and (4) separation of IO/disk intensive tasks(data staging) versus CPU/memory intensive tasks (e.g., compression,encryption, indexing/analytics).

Furthermore, since the staging cluster acts as “true-of-fact,” datawould not be removed until explicitly committed via two-phaseacknowledgement (which maps processing tasks complete state to signaldata removal). With this feature, two major components are decoupled andfault handling is simplified. Though application level consistency isfurther validated, considering its data moving use scenario, for a givenbunch of data segments, we can simply follow idempotent redo approachand the data moving result would not change.

To support multiple data sources, even different kinds of data movingpurposes (one-off migration, tiering, backup, etc.), illustrativeembodiments provide a plugin mechanism so that application details suchas status, consistent ckpt, pause/resume, are offloaded, thus making theother parts of the framework generic enough for converged and unifieddata processing and moving.

As mentioned above, in illustrative embodiments, all components aredockerized/containerized. Since we separate stateless tasks and statefultasks, CPU/memory intensive tasks and disk intensive tasks, it is easierto provision resources. With dockerization/containerization, benefitssuch as lighter package, fast running, easy deployment and management bypopular tools such as OpenStack Magnum or Docker SWARM, K8S, Mesos,etc., are realized.

FIG. 9 illustrates a processing platform used to implement a dataprocessing and data movement architecture, according to an embodiment ofthe invention.

As an example of a processing platform on which a computing environmentsuch as a cloud computing platform with data processing and datamovement functionalities (e.g., FIGS. 2-8) can be implemented isprocessing platform 900 shown in FIG. 9. It is to be appreciated thatprocessing platform 900 may implement the functionalities describedherein. For example, the various architectures and methodologies ofFIGS. 2-8 can be implemented in processing platform 900.

The processing platform 900 in this embodiment comprises a plurality ofprocessing devices, denoted 902-1, 902-2, 902-3, . . . 902-N, whichcommunicate with one another over a network 904. It is to be appreciatedthat the methodologies described herein may be executed in one suchprocessing device 902, or executed in a distributed manner across two ormore such processing devices 902. It is to be further appreciated that aserver, a client device, a computing device or any other processingplatform element may be viewed as an example of what is more generallyreferred to herein as a “processing device.” As illustrated in FIG. 9,such a device generally comprises at least one processor and anassociated memory, and implements one or more functional modules forinstantiating and/or controlling features of systems and methodologiesdescribed herein. Multiple elements or modules may be implemented by asingle processing device in a given embodiment.

The processing device 902-1 in the processing platform 900 comprises aprocessor 910 coupled to a memory 912. The processor 910 may comprise amicroprocessor, a microcontroller, an application-specific integratedcircuit (ASIC), a field programmable gate array (FPGA) or other type ofprocessing circuitry, as well as portions or combinations of suchcircuitry elements. Components of systems as disclosed herein can beimplemented at least in part in the form of one or more softwareprograms stored in memory and executed by a processor of a processingdevice such as processor 910. Memory 912 (or other storage device)having such program code embodied therein is an example of what is moregenerally referred to herein as a processor-readable storage medium.Articles of manufacture comprising such processor-readable storage mediaare considered embodiments of the invention. A given such article ofmanufacture may comprise, for example, a storage device such as astorage disk, a storage array or an integrated circuit containingmemory. The term “article of manufacture” as used herein should beunderstood to exclude transitory, propagating signals.

Furthermore, memory 912 may comprise electronic memory such as randomaccess memory (RAM), read-only memory (ROM) or other types of memory, inany combination. The one or more software programs when executed by aprocessing device such as the processing device 902-1 causes the deviceto perform functions associated with one or more of the components/stepsof system/methodologies in FIGS. 2-8. One skilled in the art would bereadily able to implement such software given the teachings providedherein. Other examples of processor-readable storage media embodyingembodiments of the invention may include, for example, optical ormagnetic disks.

Processing device 902-1 also includes network interface circuitry 914,which is used to interface the device with the network 904 and othersystem components. Such circuitry may comprise conventional transceiversof a type well known in the art.

The other processing devices 902 (902-2, 902-3, . . . 902-N) of theprocessing platform 900 are assumed to be configured in a manner similarto that shown for computing device 902-1 in the figure.

The processing platform 900 shown in FIG. 9 may comprise additionalknown components such as batch processing systems, parallel processingsystems, physical machines, virtual machines, virtual switches, storagevolumes, etc. Again, the particular processing platform shown in thisfigure is presented by way of example only, and the system shown as 900in FIG. 9 may include additional or alternative processing platforms, aswell as numerous distinct processing platforms in any combination.

Also, numerous other arrangements of servers, clients, computers,storage devices or other components are possible in processing platform900. Such components can communicate with other elements of theprocessing platform 900 over any type of network, such as a wide areanetwork (WAN), a local area network (LAN), a satellite network, atelephone or cable network, or various portions or combinations of theseand other types of networks.

It should again be emphasized that the above-described embodiments ofthe invention are presented for purposes of illustration only. Manyvariations may be made in the particular arrangements shown. Forexample, although described in the context of particular system anddevice configurations, the techniques are applicable to a wide varietyof other types of data processing systems, processing devices anddistributed virtual infrastructure arrangements. In addition, anysimplifying assumptions made above in the course of describing theillustrative embodiments should also be viewed as exemplary rather thanas requirements or limitations of the invention. Numerous otheralternative embodiments within the scope of the appended claims will bereadily apparent to those skilled in the art.

What is claimed is:
 1. A method for moving data from a source site to atarget site in a cloud computing platform, comprising: receiving aplurality of data sets to be moved from the source site to the targetsite at a plurality of containerized data ingest components located atthe source site; providing the received plurality of data sets from theplurality of data ingest components to a staging cluster comprising aplurality of containerized broker components located at the source site,wherein the plurality of containerized broker components queue theplurality of data sets, wherein the staging cluster replicates one ormore partitions of the received data set between broker components, andwherein each broker component performs a data deduplication operation;providing the queued plurality of data sets from the plurality ofcontainerized broker components to a processing cluster comprising aplurality of containerized data processing components, wherein theplurality of containerized data processing components process theplurality of data sets, wherein the processing stage performs one ormore of data encryption, data reduction, and data indexing prior to adata set being transmitted to the target site; transmitting theplurality of data sets from the plurality of containerized dataprocessing components to the target site; wherein, for each data ingestcomponent of the plurality of data ingest components, a respectivepipeline is formed through the staging cluster and the processingcluster, and wherein the staging cluster and the processing cluster arescalable such that the method further comprises: adding an additionalpipeline comprising a given containerized broker component in thestaging cluster and a given containerized data processing component inthe processing cluster when a data ingest component is added; removingan existing pipeline comprising a given containerized broker componentin the staging cluster and a given containerized data processingcomponent in the processing cluster when an existing data ingestcomponent is removed; receiving the transmitted plurality of data setsat a staging cluster comprising a plurality of containerized brokercomponents located at the target site, wherein the plurality ofcontainerized broker components queue the plurality of data sets;providing the queued plurality of data sets from the plurality ofcontainerized broker components to a processing cluster comprising aplurality of containerized data processing components; and providing theplurality of processed data sets from the plurality of containerizeddata processing components to a plurality of data applicationcomponents; wherein, for each data set received, a respective pipelineis formed through the staging cluster and the processing cluster, andwherein the staging cluster and the processing cluster are scalable toadd or remove a pipeline depending on the number of data sets received;wherein the source site and the target site are implemented via one ormore processing devices operatively coupled via a communication network.2. The method of claim 1, wherein each data ingest component receives adata moving task associated with a received data set.
 3. The method ofclaim 2, wherein each data ingest component processes the received dataset in accordance with one or more parameters of the data moving task.4. The method of claim 3, wherein the one or more parameters of the datamoving task specify one or more of: a source type of the received dataset; an identifier of the source of the received data set, target siteinformation; and one or more processes to be performed on the receiveddata set.
 5. The method of claim 3, wherein each data ingest componentlaunches one or more worker threads to read the received data set. 6.The method of claim 5, wherein each data ingest component partitions thereceived data set and sends the partitioned data set to the stagingcluster.
 7. The method of claim 6, wherein each data ingest componentpartitions the received data set in accordance with one of a content keyand an offset key.
 8. The method of claim 1, wherein each data ingestcomponent is configured to execute in accordance with an instance of anapplication program.
 9. The method of claim 8, wherein the instance ofthe application program is one of a rewritable application instance anda read-only application instance.
 10. The method of claim 1, whereineach broker component performs an asynchronous data flush operation. 11.The method of claim 1, wherein the staging cluster and the processingcluster form a directed acyclic graph structure.
 12. The method of claim1, wherein the staging cluster and the processing cluster perform atwo-phase acknowledgment procedure.
 13. The method of claim 12, whereinthe two-phase acknowledgment procedure comprises an acknowledge step anda commit step to confirm that a data set has been fully processed by theprocessing cluster.
 14. The method of claim 13, wherein the stagingcluster removes the data set once receiving confirmation that the dataset has been fully processed.
 15. The method of claim 1, wherein each ofthe data application components is executed in its own container.
 16. Asystem for moving data from a source site to a target site in a cloudcomputing platform, the system comprising: at least one processor,coupled to a memory, and configured to: receive a plurality of data setsto be moved from the source site to the target site at a plurality ofcontainerized data ingest components located at the source site; providethe received plurality of data sets from the plurality of data ingestcomponents to a staging cluster comprising a plurality of containerizedbroker components located at the source site, wherein the plurality ofcontainerized broker components queue the plurality of data sets,wherein the staging cluster replicates one or more partitions of thereceived data set between broker components, and wherein each brokercomponent performs a data deduplication operation; provide the queuedplurality of data sets from the plurality of containerized brokercomponents to a processing cluster comprising a plurality ofcontainerized data processing components, wherein the plurality ofcontainerized data processing components process the plurality of datasets, wherein the processing stage performs one or more of dataencryption, data reduction, and data indexing prior to a data set istransmitted to the target site; transmit the plurality of data sets fromthe plurality of containerized data processing components to the targetsite; wherein, for each data ingest component of the plurality of dataingest components, a respective pipeline is formed through the stagingcluster and the processing cluster, and wherein the staging cluster andthe processing cluster are scalable such that the processor is furtherconfigured to: add an additional pipeline comprising a givencontainerized broker component in the staging cluster and a givencontainerized data processing component in the processing cluster when adata ingest component is added; remove an existing pipeline comprising agiven containerized broker component in the staging cluster and a givencontainerized data processing component in the processing cluster whenan existing data ingest component is removed; receive the transmittedplurality of data sets at a staging cluster comprising a plurality ofcontainerized broker components located at the target site, wherein theplurality of containerized broker components queue the plurality of datasets; provide the queued plurality of data sets from the plurality ofcontainerized broker components to a processing cluster comprising aplurality of containerized data processing components; and provide theplurality of processed data sets from the plurality of containerizeddata processing components to a plurality of data applicationcomponents; wherein, for each data set received, a respective pipelineis formed through the staging cluster and the processing cluster, andwherein the staging cluster and the processing cluster are scalable toadd or remove a pipeline depending on the number of data sets received;wherein the source site and the target site are operatively coupled viaa communication network.
 17. The system of claim 16, wherein each dataingest component receives a data moving task associated with a receiveddata set.
 18. The system of claim 16, wherein each of the dataapplication components is executed in its own container.
 19. An articleof manufacture for moving data from a source site to a target site in acloud computing platform, the article of manufacture comprising anon-transitory processor-readable storage medium having encoded thereinexecutable code of one or more software programs, wherein the one ormore software programs when executed by at least one processing deviceimplement the steps of: receiving a plurality of data sets to be movedfrom the source site to the target site at a plurality of containerizeddata ingest components located at the source site; providing thereceived plurality of data sets from the plurality of data ingestcomponents to a staging cluster comprising a plurality of containerizedbroker components located at the source site, wherein the plurality ofcontainerized broker components queue the plurality of data sets,wherein the staging cluster replicates one or more partitions of thereceived data set between broker components, and wherein each brokercomponent performs a data deduplication operation; providing the queuedplurality of data sets from the plurality of containerized brokercomponents to a processing cluster comprising a plurality ofcontainerized data processing components, wherein the plurality ofcontainerized data processing components process the plurality of datasets, wherein the processing stage performs one or more of dataencryption, data reduction, and data indexing prior to a data set istransmitted to the target site; transmitting the plurality of data setsfrom the plurality of containerized data processing components to thetarget site; wherein, for each data ingest component of the plurality ofdata ingest components, a respective pipeline is formed through thestaging cluster and the processing cluster, and wherein the stagingcluster and the processing cluster are scalable such that theimplemented steps further comprise: adding an additional pipelinecomprising a given containerized broker component in the staging clusterand a given containerized data processing component in the processingcluster when a data ingest component is added; removing an existingpipeline comprising a given containerized broker component in thestaging cluster and a given containerized data processing component inthe processing cluster when an existing data ingest component isremoved; receiving the transmitted plurality of data sets at a stagingcluster comprising a plurality of containerized broker componentslocated at the target site, wherein the plurality of containerizedbroker components queue the plurality of data sets; providing the queuedplurality of data sets from the plurality of containerized brokercomponents to a processing cluster comprising a plurality ofcontainerized data processing components; and providing the plurality ofprocessed data sets from the plurality of containerized data processingcomponents to a plurality of data application components; wherein, foreach data set received, a respective pipeline is formed through thestaging cluster and the processing cluster, and wherein the stagingcluster and the processing cluster are scalable to add or remove apipeline depending on the number of data sets received; wherein thesource site and the target site operatively coupled via a communicationnetwork.
 20. The article of manufacture of claim 19, wherein each of thedata application components is executed in its own container.